Mechanistic-Empirical Evaluation of the Impact of Spring Load Restrictions
Bibliographic record
Abstract
The majority of Canadian geography is in a wet-freeze environment. In winter, pavements and the underlying subgrade freezes. During the spring thaw, water in the pavement structure and subgrade reduces the stiffness (resilient modulus) of some of the pavement layers and in particular the subgrade. This reduces the overall structural capacity of the pavement. In order to “protect” the pavement from excessive damage, most agencies institute a spring load restriction. During this period, the axle loads are restricted to half or three quarter the loads permitted during other periods of the year. While this practice is intended to mitigate pavement damage the application and removal of load restrictions is not typically based on a technical analysis of pavement capacity but rather on “historical dates” and it is very disruptive to the trucking industry. Over the past 10 years, Canadian highway and municipal agencies have been working on the implementation of mechanistic-empirical (M-E) designs for their roadway infrastructure. Many agencies are working on aspects of M-E design, focusing on particular aspects that have the most impact on their pavement design procedures working towards calibration of the M-E models and adoption M-E design procedures. Until recently, most Canadian roadway agencies have utilized the pavement design procedures established by the American Association of State Highway and Transportation Officials (AASHTO). The AASHTO design procedure is limited to only a few key parameters such as resilient modulus, equivalent single axle loads (ESALs), etc. The M-E design procedure is a much more robust in that it is capable of a more rigorous pavement design. This paper uses an M-E analysis to determine the impact of load restrictions on pavement damage for typical municipal roadway pavement sections from Ontario. The cost of future pavement repairs as a result of the damage is compared to the cost of the trucking industry in terms of additional trips, reduced loads, etc. Recommendations are made for an M-E analysis methodology to assist roadway agencies to mitigate pavement damage while permitting efficiencies for the trucking industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".